You know all you have to know about pie charts. And you are likely to hate or love them. But I know you are willing to accept a more balanced approach, and that’s what I’ll attempt to do here. (If you don’t know what I’m talking about and think grown ups loving or hating charts is, well, weird, don’t worry, it’s just for fun. Keep reading.)

Let’s start with a a few stats:

Google returns 2.2 million pie charts in image search, 1.8 million bar charts and only 0.34 million line charts;

Percentage of 3D pie charts in the first page: around 30%;

Percentage of pie charts with exploded slices: around 15%;

Bad pie charts (3D or exploded slices or legend or too many data points or no labels or unsorted slices): around 99%.

We can conclude that:

People love pie charts;

People love to abuse pie charts;

People don’t know how to make a pie chart;

Here is my point: we can’t tell people to stop making pie charts. It’s pointless. No one cares. A better approach is to:

Teach people how to make a pie chart;

Show them how abusing pie charts can be bad for business;

Let them decide if they still love pie charts.

What is a pie chart?

The pie is a circular chart used to display proportions of a whole. In the corporate world, market share is the obvious example of data that can be represented with pie charts. You are likely to see a lot of pies in a market research report also.

To make a pie chart your data should consist of:

Mutually exclusive, non-overlapping categories (a data point cannot belong to more than one slice);

Collectively exhaustive categories (all data points in the whole must be represented);

No negative values;

Same measurement units;

Categorical variable;

Categories must be seen as parts of a meaningful whole;

If you haven’t read the page What is a chart I recommend you do it now. I’ll wait.

Welcome back. Now you understand the origins of a pie chart:

As you can see, we first map the distances between data points, then stack them, connect them with a thick line (looks like a stacked bar chart), bend them and finally remove the hole to get the pie chart. Don’t forget to color-code each slice.

How do you read a pie chart? Research shows that people compare slices based on one of three measures: length of semi-arcs, angles and slice area:

What can we read here? The first slice accounts for around one third of the whole. Perhaps more interesting, the first two slices account for more than half of the whole.

Criticism

Pie charts are the data visualization expert’s pet peeve. Here are the major arguments against:

Research tells us that people do a very poor job at measuring angles and semi-arcs and comparing areas. The pie above confirms this: it’s very hard to say which one is the largest slice. Try it for yourself.

A second argument is that you can’t use many data points. With more than, say, five or six slices the chart becomes overcrowded and even more difficult to read. The more slices the less variation between them, so the harder to compare the arcs/angles/areas.

A third argument is that is a very low density chart. This means that the real estate needed to display a single data point is excessive. In the space taken by a pie chart with five data points we can easily display a scatter plot with hundreds of data points.

A fourth argument is that you can’t compare series, for the simple reason that you can’t use more than one. If you want to compare two or more series you have to make two or more pie charts. But if you can hardly compare slices within a pie, using multiple pies is beyond forgiveness.

Finally, the poor pie digs its own grave when it allows for the level of “creativity” as displayed below:

Due to a parallax effect, the slice “Pork” seems to be the largest. If you check the pie chart above you’ll see that that’s not the case. And because the chart is “exploded”, it’s even harder to compare angles because they no longer share the same center.

What Experts Say

So, what do experts say about this? Let’s start with Edward Tufte in The Visual Display of Quantitative Information:

A table is nearly always better than a dumb pie chart; the only worse design than a pie chart is several of them, for then the viewer is asked to compare quantities located in spatial disarray both within and between charts (…). Given their low density and failure to order numbers along a visual dimension, pie charts should never be used.

Stephen Few agrees in Show me the Numbers:

(…) allow me to declare with no further delay that I don’t use pie charts, and I strongly recommend that you abandon them as well.

And also in “Save pies for dessert”:

Of all the graphs that play major roles in the lexicon of quantitative communication, however, the pie chart is by far the least effective. Its colorful voice is often heard, but rarely understood. It mumbles when it talks.

Harsh words. Not all experts agree, though. According to Stephen Kosslyn’s Graph Design for the Eye and Mind:

Clearly, we need to temper Edward Tufte’s (1983) assertion (based solely on his intuition, as far as I can tell) that ‘the only worse design than a pie chart is several of them’ (p. 178). In some situations, this opinion is no doubt justified, but we should not make such a sweeping generalization about the value of any type of display, independent of the type of data to be displayed and the purposes to which the display will be put.

In my opinion, much of the adverse criticism of the pie has come from those who have wished it to do more than it could. The pie chart is a simple information graphic whose principal purpose is to show the relationship of a part to the whole. It is, by and large, the wrong choice as an exploratory device, and it is certainly not the correct choice when the graph maker or graph reader has a complicated purpose in mind.

If there is something that I would like to have written about pie graphs it is this “Expert notes” at ManyEyes:

Pie charts have a mixed reputation. They are popular in business and the media but many information designers have criticized the technique. Some claim that the pie slice shape communicates numbers less exactly than other possibilities such as line length. But this remains unclear in the context of proportions: for example, we have seen no studies that looked at the task of judging whether an item is more or less than 50%. It’s also unclear whether exact communication of numeric values is the only evaluation criterion; at least one study indicates that use of a pie chart for analyzing a problem as opposed to a bar chart changes the way people think about the problem.

We are having this discussion since 1926 (when the first scientific paper was published), and it’s not likely it will end soon. So, what’s a poor data analyst to do?

Don’t compare chart types. Pie charts are no better than (stacked) bar charts or the other way around. It doesn’t make sense to compare them.

If you are an innocent victim of circumstances (your boss likes and wants pie charts) the best you can do is to improve your pies.

Rescuing Pie Charts

There are no synonyms in data visualization. Using a pie chart is not the same of using a bar chart. The questions are likely to be different because of a fundamental difference: a pie chart is about parts-of-a-whole. And that make a whole difference, so to speak.

Let’s take a look at the pie chart again:

We can see that beef and chicken each account for almost one third of the total. It’s much harder to see that with a bar chart if you don’t look at the axis. On the other hand, in the pie chart you can’t see which one is bigger; that’s much simpler with a bar chart.

Let’s rearrange the slices:

When grouping slices (red meat, poultry and fish and shellfish) we can see that red meat accounts for more than half of the total. That’s something you can’t see in a bar chart. Stephen Few accepts that this is the “secret strength of pies” but he immediately dismisses it as strength that is “rarely if ever useful” and that “the fact remains that a comparison of two sets of summed parts is rare in the real world”. It may be so, but the reason is simple: we passively accept what we get and forget to analyze the data.

Don’t explode your pies: The explode option should be used to call reader’s attention to a slice. Exploding them all doesn’t make sense, because you can’t call reader’s attention to everything. But even exploding a single slice should be avoided. If there is a special slice change its border slightly. The readers will notice.

Don’t use a legend: This is a generic rule, not pie-specific. Don’t force the reader to go back and forth between the legend and the chart. Label the slices directly.

Don’t use too many chunks: Please not that I wrote “chunks” not “slices”. In the pie chart context, a “chunk” is a meaningful group of slices. Let me show you the difference:

The pie chart on the left contains a single chunk, with six slices. The pie on the right still has six slices, but we’ve grouped them into three chunks. Now we can see that red meat accounts for more than half of food availability. You can still read the slices, but now you have a new level of reading. This clearly improves your insights.

Sort the slices: you should always sort the slices from the highest to the lowest value within each chunk.

Gray out small slices: If you have too many slices you don’t really have to create an “Other” slice. Just gray them out. As a general rule, I would gray out slices below 2%. Make sure readers know these slices belong the the “Other” category. If they are not relevant you don’t even have to label them.

Pie Chart Takeaways

Humans are unable to compare angles and that’s the main scientific reason why you should not use pie charts;

I have a much more down-to-earth reason: they dumb down your message and they take up too much space for the value it provides;

If you want to make a pie chart please follow these rules:

Avoid 3D effects;

Avoid exploded slices;

Don’t compare pie charts;

Label the slices;

Group slices into meaningful chunks;

Use a color for each group and a shade for each slice;

Sort slices within each chunk;

Mute (gray-out) slices below a predefined threshold (1% or 2%);

Reality is complex and if you try to oversimplify it you’ll fail to understand it. Proportions can be a good starting point, but analyzing a business based on proportions is just lazy.

We cannot underestimate the fact that people are naturally attracted to pie charts. They effortlessly understand the metaphor; they like its colorful simplicity. This comes with a price tag attached. If you make too many pie charts you should be aware of this.

Let a pie chart have a supporting role, but it should never be the protagonist in your story.